Hi @PSK017, Let’s break down the steps for loading and using the Universal Sentence Encoder (which is a powerful pre-trained model) in your Databricks Python notebook.
Load the Universal Sentence Encoder:
- First, you’ll need to install the necessary package. Run the following command in a cell to install the required library:%%capture !pip3 install tensorflow_hub
- Next, you can load the Universal Sentence Encoder using the following code snippet:import tensorflow_hub as hub embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder-large/5")
Compute Sentence Embeddings:
- Once you’ve loaded the model, you can use it to compute embeddings for sentences. For example:sentences = [ "The quick brown fox jumps over the lazy dog.", "I am a sentence for which I would like to get its embedding." ] embeddings = embed(sentences) print(embeddings)
- The embeddings variable will contain the vector representations of your sentences.
Best Practices for Using Pre-trained Models in Databricks:
Transformers Pipelines: For many NLP tasks, you can use Hugging Face’s Transformers pipelines. These pipelines encapsulate components like tokenizers and models, making it easy to get started. They also handle GPU usage and batching efficiently. Example:
- from transformers import pipeline summarizer = pipeline("summarization", device=0 if torch.cuda.is_available() else -1)
Distribute Inference on Spark: To distribute inference on Spark, encapsulate the pipeline in a pandas UDF. Spark will automatically reassign GPUs to workers, allowing seamless multi-GPU multi-machine cluster usage.
Fine-tuning (Optional):
- If you have specific domain data, consider fine-tuning the pretrained model on your own data. Fine-tuning allows you to adapt the model to your specific task.
Remember that Databricks is an excellent platform for running Hugging Face Transformers, and you can leverage pre-trained models effectively for various NLP tasks. Feel free to explore more and adapt these practices to your specific use case! 🚀🔍
For additional best practices related to Databricks, you might also find the blog post on Super Powering Your dbt Project helpful.
